We apply the method proposed by Zheng et al. ([1]}) that combines reinforcement learning with LKH to improve the performance. The basic idea is to learn an adaptive Q-value to replace the constant \(\alpha \) -value as the metric of selecting and sorting the candidate cities, and help the algorithm learn to choose appropriate edges to be added during the \(k\) -opt process. In RHGA, the Q-LKH local search algorithm optimizes the input solution to the local optimal solution by \(k\) -opt. This subsection introduces the reinforcement learning framework in Q-LKH, the initial Q-value, the implementation of Q-learning algorithm, and the detailed \(k\) -opt process in Q-LKH.
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